{
 "cells": [
  {
   "cell_type": "raw",
   "id": "fbc66410",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: Bedrock Chat\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
    "# BedrockChat\n",
    "\n",
    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
    "> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
    "> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
    "> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
    "> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
    "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
    "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
    "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
    "> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d51edc81",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import BedrockChat\n",
    "from langchain.schema import HumanMessage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\": 0.1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [\n",
    "    HumanMessage(\n",
    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
    "    )\n",
    "]\n",
    "chat(messages)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a4a4f4d4",
   "metadata": {},
   "source": [
    "### For BedrockChat with Streaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c253883f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "\n",
    "chat = BedrockChat(\n",
    "    model_id=\"anthropic.claude-v2\",\n",
    "    streaming=True,\n",
    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
    "    model_kwargs={\"temperature\": 0.1},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9e52838",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    HumanMessage(\n",
    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
    "    )\n",
    "]\n",
    "chat(messages)"
   ]
  }
 ],
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